Overview

Brought to you by YData

Dataset statistics

Number of variables27
Number of observations1043
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory1.4 KiB

Variable types

Text4
Numeric3
Categorical19
Boolean1

Alerts

fax has constant value "?" Constant
Rambience is highly overall correlated with other_servicesHigh correlation
Rcuisine is highly overall correlated with accessibility and 5 other fieldsHigh correlation
accessibility is highly overall correlated with Rcuisine and 4 other fieldsHigh correlation
alcohol is highly overall correlated with Rcuisine and 2 other fieldsHigh correlation
area is highly overall correlated with city and 3 other fieldsHigh correlation
city is highly overall correlated with accessibility and 7 other fieldsHigh correlation
country is highly overall correlated with city and 2 other fieldsHigh correlation
dress_code is highly overall correlated with urlHigh correlation
food_rating is highly overall correlated with rating and 1 other fieldsHigh correlation
franchise is highly overall correlated with Rcuisine and 1 other fieldsHigh correlation
latitude is highly overall correlated with city and 2 other fieldsHigh correlation
longitude is highly overall correlated with city and 2 other fieldsHigh correlation
other_services is highly overall correlated with Rambience and 1 other fieldsHigh correlation
parking_lot is highly overall correlated with RcuisineHigh correlation
placeID is highly overall correlated with Rcuisine and 4 other fieldsHigh correlation
price is highly overall correlated with zipHigh correlation
rating is highly overall correlated with food_rating and 1 other fieldsHigh correlation
service_rating is highly overall correlated with food_rating and 1 other fieldsHigh correlation
smoking_area is highly overall correlated with areaHigh correlation
state is highly overall correlated with accessibility and 7 other fieldsHigh correlation
url is highly overall correlated with alcohol and 2 other fieldsHigh correlation
zip is highly overall correlated with accessibility and 11 other fieldsHigh correlation
dress_code is highly imbalanced (65.1%) Imbalance
url is highly imbalanced (70.6%) Imbalance
Rambience is highly imbalanced (62.3%) Imbalance
other_services is highly imbalanced (74.3%) Imbalance

Reproduction

Analysis started2024-11-08 13:29:54.461530
Analysis finished2024-11-08 13:29:55.488341
Duration1.03 second
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

userID
Text

Distinct138
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Memory size63.3 KiB
2024-11-08T10:29:55.599303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5215
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st rowU1077
2nd rowU1077
3rd rowU1077
4th rowU1068
5th rowU1068
ValueCountFrequency (%)
u1061 20
 
1.9%
u1106 18
 
1.7%
u1024 16
 
1.5%
u1097 16
 
1.5%
u1135 15
 
1.4%
u1071 15
 
1.4%
u1081 14
 
1.3%
u1089 13
 
1.2%
u1134 13
 
1.2%
u1104 13
 
1.2%
Other values (128) 890
85.3%
2024-11-08T10:29:55.809607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1597
30.6%
U 1043
20.0%
0 971
18.6%
3 248
 
4.8%
2 247
 
4.7%
8 205
 
3.9%
6 191
 
3.7%
9 189
 
3.6%
5 188
 
3.6%
4 180
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1597
30.6%
U 1043
20.0%
0 971
18.6%
3 248
 
4.8%
2 247
 
4.7%
8 205
 
3.9%
6 191
 
3.7%
9 189
 
3.6%
5 188
 
3.6%
4 180
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1597
30.6%
U 1043
20.0%
0 971
18.6%
3 248
 
4.8%
2 247
 
4.7%
8 205
 
3.9%
6 191
 
3.7%
9 189
 
3.6%
5 188
 
3.6%
4 180
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1597
30.6%
U 1043
20.0%
0 971
18.6%
3 248
 
4.8%
2 247
 
4.7%
8 205
 
3.9%
6 191
 
3.7%
9 189
 
3.6%
5 188
 
3.6%
4 180
 
3.5%

placeID
Real number (ℝ)

High correlation 

Distinct95
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134158.12
Minimum132560
Maximum135109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.3 KiB
2024-11-08T10:29:55.915722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum132560
5-th percentile132626
Q1132856
median135028
Q3135053
95-th percentile135085
Maximum135109
Range2549
Interquartile range (IQR)2197

Descriptive statistics

Standard deviation1105.2629
Coefficient of variation (CV)0.008238509
Kurtosis-1.7901527
Mean134158.12
Median Absolute Deviation (MAD)51
Skewness-0.44126027
Sum1.3992692 × 108
Variance1221606.1
MonotonicityNot monotonic
2024-11-08T10:29:55.949837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135032 56
 
5.4%
135052 50
 
4.8%
135085 36
 
3.5%
135041 34
 
3.3%
132825 32
 
3.1%
132834 25
 
2.4%
135030 24
 
2.3%
135053 24
 
2.3%
135026 22
 
2.1%
135060 22
 
2.1%
Other values (85) 718
68.8%
ValueCountFrequency (%)
132560 4
 
0.4%
132572 15
1.4%
132583 4
 
0.4%
132584 6
 
0.6%
132594 5
 
0.5%
132608 6
 
0.6%
132609 5
 
0.5%
132613 6
 
0.6%
132626 4
 
0.4%
132630 6
 
0.6%
ValueCountFrequency (%)
135109 4
 
0.4%
135106 10
 
1.0%
135104 7
 
0.7%
135088 6
 
0.6%
135086 20
1.9%
135085 36
3.5%
135079 17
1.6%
135075 13
 
1.2%
135074 4
 
0.4%
135073 8
 
0.8%

rating
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size59.2 KiB
2
446 
1
376 
0
221 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1043
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
2 446
42.8%
1 376
36.0%
0 221
21.2%

Length

2024-11-08T10:29:55.977259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T10:29:56.007448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2 446
42.8%
1 376
36.0%
0 221
21.2%

Most occurring characters

ValueCountFrequency (%)
2 446
42.8%
1 376
36.0%
0 221
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1043
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 446
42.8%
1 376
36.0%
0 221
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1043
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 446
42.8%
1 376
36.0%
0 221
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1043
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 446
42.8%
1 376
36.0%
0 221
21.2%

food_rating
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size59.2 KiB
2
467 
1
332 
0
244 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1043
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row0

Common Values

ValueCountFrequency (%)
2 467
44.8%
1 332
31.8%
0 244
23.4%

Length

2024-11-08T10:29:56.030546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T10:29:56.056286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2 467
44.8%
1 332
31.8%
0 244
23.4%

Most occurring characters

ValueCountFrequency (%)
2 467
44.8%
1 332
31.8%
0 244
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1043
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 467
44.8%
1 332
31.8%
0 244
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1043
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 467
44.8%
1 332
31.8%
0 244
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1043
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 467
44.8%
1 332
31.8%
0 244
23.4%

service_rating
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size59.2 KiB
1
392 
2
378 
0
273 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1043
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row0

Common Values

ValueCountFrequency (%)
1 392
37.6%
2 378
36.2%
0 273
26.2%

Length

2024-11-08T10:29:56.081157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T10:29:56.106503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 392
37.6%
2 378
36.2%
0 273
26.2%

Most occurring characters

ValueCountFrequency (%)
1 392
37.6%
2 378
36.2%
0 273
26.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1043
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 392
37.6%
2 378
36.2%
0 273
26.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1043
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 392
37.6%
2 378
36.2%
0 273
26.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1043
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 392
37.6%
2 378
36.2%
0 273
26.2%

Rcuisine
Categorical

High correlation 

Distinct23
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size66.3 KiB
Mexican
238 
Bar
140 
Cafeteria
102 
Fast_Food
91 
Seafood
62 
Other values (18)
410 

Length

Max length16
Median length15
Mean length7.9568552
Min length3

Characters and Unicode

Total characters8299
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFast_Food
2nd rowMexican
3rd rowSeafood
4th rowMexican
5th rowMexican

Common Values

ValueCountFrequency (%)
Mexican 238
22.8%
Bar 140
13.4%
Cafeteria 102
9.8%
Fast_Food 91
 
8.7%
Seafood 62
 
5.9%
Bar_Pub_Brewery 59
 
5.7%
Pizzeria 51
 
4.9%
Chinese 41
 
3.9%
American 39
 
3.7%
International 37
 
3.5%
Other values (13) 183
17.5%

Length

2024-11-08T10:29:56.189155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mexican 238
22.8%
bar 140
13.4%
cafeteria 102
9.8%
fast_food 91
 
8.7%
seafood 62
 
5.9%
bar_pub_brewery 59
 
5.7%
pizzeria 51
 
4.9%
chinese 41
 
3.9%
american 39
 
3.7%
international 37
 
3.5%
Other values (13) 183
17.5%

Most occurring characters

ValueCountFrequency (%)
a 1178
14.2%
e 1047
12.6%
r 707
 
8.5%
i 617
 
7.4%
n 551
 
6.6%
o 435
 
5.2%
t 344
 
4.1%
B 312
 
3.8%
c 286
 
3.4%
M 242
 
2.9%
Other values (27) 2580
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8299
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1178
14.2%
e 1047
12.6%
r 707
 
8.5%
i 617
 
7.4%
n 551
 
6.6%
o 435
 
5.2%
t 344
 
4.1%
B 312
 
3.8%
c 286
 
3.4%
M 242
 
2.9%
Other values (27) 2580
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8299
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1178
14.2%
e 1047
12.6%
r 707
 
8.5%
i 617
 
7.4%
n 551
 
6.6%
o 435
 
5.2%
t 344
 
4.1%
B 312
 
3.8%
c 286
 
3.4%
M 242
 
2.9%
Other values (27) 2580
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8299
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1178
14.2%
e 1047
12.6%
r 707
 
8.5%
i 617
 
7.4%
n 551
 
6.6%
o 435
 
5.2%
t 344
 
4.1%
B 312
 
3.8%
c 286
 
3.4%
M 242
 
2.9%
Other values (27) 2580
31.1%

latitude
Real number (ℝ)

High correlation 

Distinct95
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.014881
Minimum18.859803
Maximum23.760268
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.3 KiB
2024-11-08T10:29:56.222254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum18.859803
5-th percentile18.921785
Q122.142273
median22.150305
Q322.156376
95-th percentile23.752511
Maximum23.760268
Range4.9004653
Interquartile range (IQR)0.0141028

Descriptive statistics

Standard deviation1.0928133
Coefficient of variation (CV)0.049639757
Kurtosis3.6060136
Mean22.014881
Median Absolute Deviation (MAD)0.006164
Skewness-1.6938269
Sum22961.521
Variance1.194241
MonotonicityNot monotonic
2024-11-08T10:29:56.259775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.152481 56
 
5.4%
22.150981 50
 
4.8%
22.150802 36
 
3.5%
22.15106 34
 
3.3%
22.1473922 32
 
3.1%
22.156469 25
 
2.4%
22.14788 24
 
2.3%
22.178931 24
 
2.3%
22.148665 22
 
2.1%
22.156883 22
 
2.1%
Other values (85) 718
68.8%
ValueCountFrequency (%)
18.859803 4
 
0.4%
18.8699929 8
0.8%
18.875011 6
0.6%
18.8760113 6
0.6%
18.8820871 6
0.6%
18.9101777 3
 
0.3%
18.915421 5
0.5%
18.916654 12
1.2%
18.9217848 4
 
0.4%
18.9222904 4
 
0.4%
ValueCountFrequency (%)
23.7602683 5
0.5%
23.7588052 6
0.6%
23.7543569 8
0.8%
23.7529821 7
0.7%
23.7529305 6
0.6%
23.7529035 6
0.6%
23.7527071 10
1.0%
23.7526973 4
 
0.4%
23.7525107 6
0.6%
23.7523648 6
0.6%

longitude
Real number (ℝ)

High correlation 

Distinct94
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-100.63817
Minimum-101.0286
Maximum-99.132357
Zeros0
Zeros (%)0.0%
Negative1043
Negative (%)100.0%
Memory size8.3 KiB
2024-11-08T10:29:56.294949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-101.0286
5-th percentile-101.01395
Q1-100.98947
median-100.97766
Q3-100.93457
95-th percentile-99.164473
Maximum-99.132357
Range1.8962429
Interquartile range (IQR)0.0548984

Descriptive statistics

Standard deviation0.70508142
Coefficient of variation (CV)-0.0070061036
Kurtosis0.48771964
Mean-100.63817
Median Absolute Deviation (MAD)0.0192232
Skewness1.5741086
Sum-104965.61
Variance0.49713981
MonotonicityNot monotonic
2024-11-08T10:29:56.334514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-100.973486 56
 
5.4%
-100.977412 50
 
4.8%
-101.001273 40
 
3.8%
-100.98268 36
 
3.5%
-100.977659 34
 
3.3%
-100.983092 32
 
3.1%
-100.98554 25
 
2.4%
-101.012861 24
 
2.3%
-100.989472 24
 
2.3%
-100.978485 22
 
2.1%
Other values (84) 700
67.1%
ValueCountFrequency (%)
-101.0286 5
 
0.5%
-101.021422 5
 
0.5%
-101.019845 12
1.2%
-101.0195459 5
 
0.5%
-101.018032 4
 
0.4%
-101.0173023 7
 
0.7%
-101.013955 20
1.9%
-101.012861 24
2.3%
-101.0102749 5
 
0.5%
-101.008472 7
 
0.7%
ValueCountFrequency (%)
-99.1323571 4
 
0.4%
-99.1342413 5
0.5%
-99.1351318 4
 
0.4%
-99.1504365 3
 
0.3%
-99.1519547 3
 
0.3%
-99.1586602 4
 
0.4%
-99.159422 6
0.6%
-99.1625655 10
1.0%
-99.1630268 6
0.6%
-99.1633594 4
 
0.4%
Distinct95
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size109.1 KiB
2024-11-08T10:29:56.460923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length50
Median length50
Mean length50
Min length50

Characters and Unicode

Total characters52150
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0101000020957F00009F823DA6094858C18A2D4D37F9A44B41
2nd row0101000020957F00001AD016568C4858C1243261274BA54B41
3rd row0101000020957F00004C95C918394758C17A5C44896AA34B41
4th row0101000020957F00007CDF5EAFC58157C1645743B23E4F4941
5th row0101000020957F000027A30471EE8157C1AC17D61EC84E4941
ValueCountFrequency (%)
0101000020957f000017d69ff5084858c1a8f2188740a24b41 56
 
5.4%
0101000020957f0000b72147f1274858c189a406ed75a34b41 50
 
4.8%
0101000020957f00009f823da6094858c18a2d4d37f9a44b41 36
 
3.5%
0101000020957f0000d2002719234858c1cc7a9fc186a34b41 34
 
3.3%
0101000020957f00001ad016568c4858c1243261274ba54b41 32
 
3.1%
0101000020957f00007768135a174758c155d318cb73a54b41 25
 
2.4%
0101000020957f0000c57c76034c4858c10e437a7e15a74b41 24
 
2.3%
0101000020957f0000b3c05ab5e54258c180f99573e7ab4b41 24
 
2.3%
0101000020957f0000c75f8790d94758c1ff8bca0567aa4b41 22
 
2.1%
0101000020957f00004c95c918394758c17a5c44896aa34b41 22
 
2.1%
Other values (85) 718
68.8%
2024-11-08T10:29:56.683795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 12824
24.6%
1 5844
11.2%
4 4391
 
8.4%
5 3594
 
6.9%
7 3090
 
5.9%
8 2937
 
5.6%
9 2744
 
5.3%
2 2531
 
4.9%
F 2477
 
4.7%
C 2400
 
4.6%
Other values (6) 9318
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12824
24.6%
1 5844
11.2%
4 4391
 
8.4%
5 3594
 
6.9%
7 3090
 
5.9%
8 2937
 
5.6%
9 2744
 
5.3%
2 2531
 
4.9%
F 2477
 
4.7%
C 2400
 
4.6%
Other values (6) 9318
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12824
24.6%
1 5844
11.2%
4 4391
 
8.4%
5 3594
 
6.9%
7 3090
 
5.9%
8 2937
 
5.6%
9 2744
 
5.3%
2 2531
 
4.9%
F 2477
 
4.7%
C 2400
 
4.6%
Other values (6) 9318
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12824
24.6%
1 5844
11.2%
4 4391
 
8.4%
5 3594
 
6.9%
7 3090
 
5.9%
8 2937
 
5.6%
9 2744
 
5.3%
2 2531
 
4.9%
F 2477
 
4.7%
C 2400
 
4.6%
Other values (6) 9318
17.9%

name
Text

Distinct94
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size79.4 KiB
2024-11-08T10:29:56.874113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length52
Median length32
Mean length19.949185
Min length3

Characters and Unicode

Total characters20807
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTortas Locas Hipocampo
2nd rowpuesto de tacos
3rd rowRestaurante Marisco Sam
4th rowvips
5th rowCarreton de Flautas y Migadas
ValueCountFrequency (%)
restaurante 210
 
6.6%
la 195
 
6.1%
el 181
 
5.7%
restaurant 144
 
4.5%
de 133
 
4.2%
cafe 103
 
3.2%
cantina 90
 
2.8%
y 85
 
2.7%
pizza 66
 
2.1%
bar 66
 
2.1%
Other values (147) 1913
60.0%
2024-11-08T10:29:57.096208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3060
14.7%
2160
 
10.4%
e 1629
 
7.8%
t 1316
 
6.3%
r 1252
 
6.0%
i 1230
 
5.9%
s 1184
 
5.7%
n 1184
 
5.7%
o 1108
 
5.3%
u 712
 
3.4%
Other values (47) 5972
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20807
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3060
14.7%
2160
 
10.4%
e 1629
 
7.8%
t 1316
 
6.3%
r 1252
 
6.0%
i 1230
 
5.9%
s 1184
 
5.7%
n 1184
 
5.7%
o 1108
 
5.3%
u 712
 
3.4%
Other values (47) 5972
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20807
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3060
14.7%
2160
 
10.4%
e 1629
 
7.8%
t 1316
 
6.3%
r 1252
 
6.0%
i 1230
 
5.9%
s 1184
 
5.7%
n 1184
 
5.7%
o 1108
 
5.3%
u 712
 
3.4%
Other values (47) 5972
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20807
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3060
14.7%
2160
 
10.4%
e 1629
 
7.8%
t 1316
 
6.3%
r 1252
 
6.0%
i 1230
 
5.9%
s 1184
 
5.7%
n 1184
 
5.7%
o 1108
 
5.3%
u 712
 
3.4%
Other values (47) 5972
28.7%
Distinct71
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size80.0 KiB
2024-11-08T10:29:57.297064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length47
Median length39
Mean length21.392138
Min length1

Characters and Unicode

Total characters22312
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVenustiano Carranza 719 Centro
2nd rowesquina santos degollado y leon guzman
3rd rowIgnacio Allende 785 Centro
4th row?
5th row?
ValueCountFrequency (%)
centro 227
 
6.1%
166
 
4.5%
de 128
 
3.4%
carranza 109
 
2.9%
ignacio 72
 
1.9%
venustiano 69
 
1.9%
y 66
 
1.8%
300 60
 
1.6%
av 60
 
1.6%
tangamanga 59
 
1.6%
Other values (184) 2704
72.7%
2024-11-08T10:29:57.527943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2687
 
12.0%
a 2509
 
11.2%
n 1579
 
7.1%
e 1560
 
7.0%
o 1437
 
6.4%
r 1192
 
5.3%
i 973
 
4.4%
t 913
 
4.1%
l 913
 
4.1%
s 663
 
3.0%
Other values (50) 7886
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22312
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2687
 
12.0%
a 2509
 
11.2%
n 1579
 
7.1%
e 1560
 
7.0%
o 1437
 
6.4%
r 1192
 
5.3%
i 973
 
4.4%
t 913
 
4.1%
l 913
 
4.1%
s 663
 
3.0%
Other values (50) 7886
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22312
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2687
 
12.0%
a 2509
 
11.2%
n 1579
 
7.1%
e 1560
 
7.0%
o 1437
 
6.4%
r 1192
 
5.3%
i 973
 
4.4%
t 913
 
4.1%
l 913
 
4.1%
s 663
 
3.0%
Other values (50) 7886
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22312
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2687
 
12.0%
a 2509
 
11.2%
n 1579
 
7.1%
e 1560
 
7.0%
o 1437
 
6.4%
r 1192
 
5.3%
i 973
 
4.4%
t 913
 
4.1%
l 913
 
4.1%
s 663
 
3.0%
Other values (50) 7886
35.3%

city
Categorical

High correlation 

Distinct16
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size70.9 KiB
San Luis Potosi
675 
?
89 
Cuernavaca
 
57
san luis potosi
 
49
victoria
 
45
Other values (11)
128 

Length

Max length16
Median length15
Mean length12.507191
Min length1

Characters and Unicode

Total characters13045
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSan Luis Potosi
2nd rows.l.p.
3rd rowSan Luis Potosi
4th row?
5th rowCiudad Victoria

Common Values

ValueCountFrequency (%)
San Luis Potosi 675
64.7%
? 89
 
8.5%
Cuernavaca 57
 
5.5%
san luis potosi 49
 
4.7%
victoria 45
 
4.3%
s.l.p. 32
 
3.1%
Ciudad Victoria 18
 
1.7%
Soledad 18
 
1.7%
Jiutepec 12
 
1.2%
s.l.p 12
 
1.2%
Other values (6) 36
 
3.5%

Length

2024-11-08T10:29:57.633307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
san 734
28.9%
luis 734
28.9%
potosi 729
28.7%
89
 
3.5%
victoria 79
 
3.1%
cuernavaca 67
 
2.6%
s.l.p 44
 
1.7%
ciudad 18
 
0.7%
soledad 18
 
0.7%
jiutepec 12
 
0.5%
Other values (2) 17
 
0.7%

Most occurring characters

ValueCountFrequency (%)
i 1651
12.7%
s 1571
12.0%
o 1565
12.0%
1507
11.6%
a 1050
8.0%
u 831
6.4%
t 825
6.3%
n 801
6.1%
S 693
 
5.3%
P 675
 
5.2%
Other values (13) 1876
14.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13045
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1651
12.7%
s 1571
12.0%
o 1565
12.0%
1507
11.6%
a 1050
8.0%
u 831
6.4%
t 825
6.3%
n 801
6.1%
S 693
 
5.3%
P 675
 
5.2%
Other values (13) 1876
14.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13045
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1651
12.7%
s 1571
12.0%
o 1565
12.0%
1507
11.6%
a 1050
8.0%
u 831
6.4%
t 825
6.3%
n 801
6.1%
S 693
 
5.3%
P 675
 
5.2%
Other values (13) 1876
14.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13045
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1651
12.7%
s 1571
12.0%
o 1565
12.0%
1507
11.6%
a 1050
8.0%
u 831
6.4%
t 825
6.3%
n 801
6.1%
S 693
 
5.3%
P 675
 
5.2%
Other values (13) 1876
14.4%

state
Categorical

High correlation 

Distinct13
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size64.3 KiB
SLP
550 
San Luis Potosi
137 
?
77 
Morelos
69 
Tamaulipas
 
47
Other values (8)
163 

Length

Max length15
Median length3
Mean length6.012464
Min length1

Characters and Unicode

Total characters6271
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSLP
2nd rows.l.p.
3rd rowSLP
4th row?
5th rowTamaulipas

Common Values

ValueCountFrequency (%)
SLP 550
52.7%
San Luis Potosi 137
 
13.1%
? 77
 
7.4%
Morelos 69
 
6.6%
Tamaulipas 47
 
4.5%
san luis potosi 43
 
4.1%
s.l.p. 32
 
3.1%
tamaulipas 32
 
3.1%
S.L.P. 18
 
1.7%
mexico 17
 
1.6%
Other values (3) 21
 
2.0%

Length

2024-11-08T10:29:57.661213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
slp 555
39.2%
san 186
 
13.1%
luis 186
 
13.1%
potosi 180
 
12.7%
morelos 79
 
5.6%
tamaulipas 79
 
5.6%
77
 
5.4%
s.l.p 50
 
3.5%
mexico 17
 
1.2%
potos 6
 
0.4%

Most occurring characters

ValueCountFrequency (%)
S 705
11.2%
L 705
11.2%
P 705
11.2%
s 616
9.8%
o 547
8.7%
i 462
 
7.4%
a 423
 
6.7%
372
 
5.9%
u 265
 
4.2%
l 244
 
3.9%
Other values (12) 1227
19.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6271
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 705
11.2%
L 705
11.2%
P 705
11.2%
s 616
9.8%
o 547
8.7%
i 462
 
7.4%
a 423
 
6.7%
372
 
5.9%
u 265
 
4.2%
l 244
 
3.9%
Other values (12) 1227
19.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6271
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 705
11.2%
L 705
11.2%
P 705
11.2%
s 616
9.8%
o 547
8.7%
i 462
 
7.4%
a 423
 
6.7%
372
 
5.9%
u 265
 
4.2%
l 244
 
3.9%
Other values (12) 1227
19.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6271
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 705
11.2%
L 705
11.2%
P 705
11.2%
s 616
9.8%
o 547
8.7%
i 462
 
7.4%
a 423
 
6.7%
372
 
5.9%
u 265
 
4.2%
l 244
 
3.9%
Other values (12) 1227
19.6%

country
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size63.7 KiB
Mexico
802 
?
131 
mexico
110 

Length

Max length6
Median length6
Mean length5.3720038
Min length1

Characters and Unicode

Total characters5603
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMexico
2nd rowmexico
3rd rowMexico
4th row?
5th rowMexico

Common Values

ValueCountFrequency (%)
Mexico 802
76.9%
? 131
 
12.6%
mexico 110
 
10.5%

Length

2024-11-08T10:29:57.686168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T10:29:57.711352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
mexico 912
87.4%
131
 
12.6%

Most occurring characters

ValueCountFrequency (%)
e 912
16.3%
x 912
16.3%
i 912
16.3%
c 912
16.3%
o 912
16.3%
M 802
14.3%
? 131
 
2.3%
m 110
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5603
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 912
16.3%
x 912
16.3%
i 912
16.3%
c 912
16.3%
o 912
16.3%
M 802
14.3%
? 131
 
2.3%
m 110
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5603
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 912
16.3%
x 912
16.3%
i 912
16.3%
c 912
16.3%
o 912
16.3%
M 802
14.3%
? 131
 
2.3%
m 110
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5603
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 912
16.3%
x 912
16.3%
i 912
16.3%
c 912
16.3%
o 912
16.3%
M 802
14.3%
? 131
 
2.3%
m 110
 
2.0%

fax
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size59.2 KiB
?
1043 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1043
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row?
2nd row?
3rd row?
4th row?
5th row?

Common Values

ValueCountFrequency (%)
? 1043
100.0%

Length

2024-11-08T10:29:57.734076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T10:29:57.757056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1043
100.0%

Most occurring characters

ValueCountFrequency (%)
? 1043
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1043
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
? 1043
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1043
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
? 1043
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1043
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
? 1043
100.0%

zip
Categorical

High correlation 

Distinct27
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size61.4 KiB
?
494 
78000
181 
78269
 
39
78280
 
32
78250
 
31
Other values (22)
266 

Length

Max length6
Median length5
Mean length3.1150527
Min length1

Characters and Unicode

Total characters3249
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row78000
2nd row78280
3rd row78310
4th row?
5th row?

Common Values

ValueCountFrequency (%)
? 494
47.4%
78000 181
 
17.4%
78269 39
 
3.7%
78280 32
 
3.1%
78250 31
 
3.0%
78349 25
 
2.4%
62460 24
 
2.3%
78310 22
 
2.1%
78290 20
 
1.9%
78396 18
 
1.7%
Other values (17) 157
 
15.1%

Length

2024-11-08T10:29:57.778675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
494
47.4%
78000 181
 
17.4%
78269 39
 
3.7%
78280 32
 
3.1%
78250 31
 
3.0%
78349 25
 
2.4%
62460 24
 
2.3%
78310 22
 
2.1%
78290 20
 
1.9%
78396 18
 
1.7%
Other values (17) 157
 
15.1%

Most occurring characters

ValueCountFrequency (%)
0 894
27.5%
8 502
15.5%
? 494
15.2%
7 481
14.8%
2 260
 
8.0%
9 162
 
5.0%
6 156
 
4.8%
3 124
 
3.8%
4 81
 
2.5%
1 44
 
1.4%
Other values (2) 51
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 894
27.5%
8 502
15.5%
? 494
15.2%
7 481
14.8%
2 260
 
8.0%
9 162
 
5.0%
6 156
 
4.8%
3 124
 
3.8%
4 81
 
2.5%
1 44
 
1.4%
Other values (2) 51
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 894
27.5%
8 502
15.5%
? 494
15.2%
7 481
14.8%
2 260
 
8.0%
9 162
 
5.0%
6 156
 
4.8%
3 124
 
3.8%
4 81
 
2.5%
1 44
 
1.4%
Other values (2) 51
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 894
27.5%
8 502
15.5%
? 494
15.2%
7 481
14.8%
2 260
 
8.0%
9 162
 
5.0%
6 156
 
4.8%
3 124
 
3.8%
4 81
 
2.5%
1 44
 
1.4%
Other values (2) 51
 
1.6%

alcohol
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size72.2 KiB
No_Alcohol_Served
637 
Wine-Beer
279 
Full_Bar
127 

Length

Max length17
Median length17
Mean length13.764142
Min length8

Characters and Unicode

Total characters14356
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo_Alcohol_Served
2nd rowNo_Alcohol_Served
3rd rowNo_Alcohol_Served
4th rowFull_Bar
5th rowNo_Alcohol_Served

Common Values

ValueCountFrequency (%)
No_Alcohol_Served 637
61.1%
Wine-Beer 279
26.7%
Full_Bar 127
 
12.2%

Length

2024-11-08T10:29:57.803762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T10:29:57.830204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no_alcohol_served 637
61.1%
wine-beer 279
26.7%
full_bar 127
 
12.2%

Most occurring characters

ValueCountFrequency (%)
e 2111
14.7%
o 1911
13.3%
l 1528
10.6%
_ 1401
9.8%
r 1043
 
7.3%
N 637
 
4.4%
d 637
 
4.4%
v 637
 
4.4%
S 637
 
4.4%
h 637
 
4.4%
Other values (10) 3177
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14356
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2111
14.7%
o 1911
13.3%
l 1528
10.6%
_ 1401
9.8%
r 1043
 
7.3%
N 637
 
4.4%
d 637
 
4.4%
v 637
 
4.4%
S 637
 
4.4%
h 637
 
4.4%
Other values (10) 3177
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14356
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2111
14.7%
o 1911
13.3%
l 1528
10.6%
_ 1401
9.8%
r 1043
 
7.3%
N 637
 
4.4%
d 637
 
4.4%
v 637
 
4.4%
S 637
 
4.4%
h 637
 
4.4%
Other values (10) 3177
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14356
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2111
14.7%
o 1911
13.3%
l 1528
10.6%
_ 1401
9.8%
r 1043
 
7.3%
N 637
 
4.4%
d 637
 
4.4%
v 637
 
4.4%
S 637
 
4.4%
h 637
 
4.4%
Other values (10) 3177
22.1%

smoking_area
Categorical

High correlation 

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size65.4 KiB
none
525 
not permitted
247 
section
208 
permitted
53 
only at bar
 
10

Length

Max length13
Median length4
Mean length7.050815
Min length4

Characters and Unicode

Total characters7354
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot permitted
2nd rownone
3rd rownone
4th rownot permitted
5th rowpermitted

Common Values

ValueCountFrequency (%)
none 525
50.3%
not permitted 247
23.7%
section 208
 
19.9%
permitted 53
 
5.1%
only at bar 10
 
1.0%

Length

2024-11-08T10:29:57.912299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T10:29:57.940811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
none 525
40.1%
permitted 300
22.9%
not 247
18.9%
section 208
 
15.9%
only 10
 
0.8%
at 10
 
0.8%
bar 10
 
0.8%

Most occurring characters

ValueCountFrequency (%)
n 1515
20.6%
e 1333
18.1%
t 1065
14.5%
o 990
13.5%
i 508
 
6.9%
r 310
 
4.2%
p 300
 
4.1%
m 300
 
4.1%
d 300
 
4.1%
267
 
3.6%
Other values (6) 466
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7354
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1515
20.6%
e 1333
18.1%
t 1065
14.5%
o 990
13.5%
i 508
 
6.9%
r 310
 
4.2%
p 300
 
4.1%
m 300
 
4.1%
d 300
 
4.1%
267
 
3.6%
Other values (6) 466
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7354
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1515
20.6%
e 1333
18.1%
t 1065
14.5%
o 990
13.5%
i 508
 
6.9%
r 310
 
4.2%
p 300
 
4.1%
m 300
 
4.1%
d 300
 
4.1%
267
 
3.6%
Other values (6) 466
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7354
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1515
20.6%
e 1333
18.1%
t 1065
14.5%
o 990
13.5%
i 508
 
6.9%
r 310
 
4.2%
p 300
 
4.1%
m 300
 
4.1%
d 300
 
4.1%
267
 
3.6%
Other values (6) 466
 
6.3%

dress_code
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size66.1 KiB
informal
930 
casual
99 
formal
 
14

Length

Max length8
Median length8
Mean length7.7833174
Min length6

Characters and Unicode

Total characters8118
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowinformal
2nd rowinformal
3rd rowinformal
4th rowinformal
5th rowinformal

Common Values

ValueCountFrequency (%)
informal 930
89.2%
casual 99
 
9.5%
formal 14
 
1.3%

Length

2024-11-08T10:29:57.967610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T10:29:57.994829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
informal 930
89.2%
casual 99
 
9.5%
formal 14
 
1.3%

Most occurring characters

ValueCountFrequency (%)
a 1142
14.1%
l 1043
12.8%
f 944
11.6%
o 944
11.6%
r 944
11.6%
m 944
11.6%
i 930
11.5%
n 930
11.5%
c 99
 
1.2%
s 99
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8118
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1142
14.1%
l 1043
12.8%
f 944
11.6%
o 944
11.6%
r 944
11.6%
m 944
11.6%
i 930
11.5%
n 930
11.5%
c 99
 
1.2%
s 99
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8118
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1142
14.1%
l 1043
12.8%
f 944
11.6%
o 944
11.6%
r 944
11.6%
m 944
11.6%
i 930
11.5%
n 930
11.5%
c 99
 
1.2%
s 99
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8118
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1142
14.1%
l 1043
12.8%
f 944
11.6%
o 944
11.6%
r 944
11.6%
m 944
11.6%
i 930
11.5%
n 930
11.5%
c 99
 
1.2%
s 99
 
1.2%

accessibility
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
no_accessibility
681 
completely
290 
partially
72 

Length

Max length16
Median length16
Mean length13.848514
Min length9

Characters and Unicode

Total characters14444
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno_accessibility
2nd rowcompletely
3rd rowno_accessibility
4th rowcompletely
5th rowcompletely

Common Values

ValueCountFrequency (%)
no_accessibility 681
65.3%
completely 290
27.8%
partially 72
 
6.9%

Length

2024-11-08T10:29:58.020324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T10:29:58.047698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no_accessibility 681
65.3%
completely 290
27.8%
partially 72
 
6.9%

Most occurring characters

ValueCountFrequency (%)
i 2115
14.6%
c 1652
11.4%
l 1405
9.7%
s 1362
9.4%
e 1261
8.7%
t 1043
7.2%
y 1043
7.2%
o 971
6.7%
a 825
 
5.7%
n 681
 
4.7%
Other values (5) 2086
14.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 2115
14.6%
c 1652
11.4%
l 1405
9.7%
s 1362
9.4%
e 1261
8.7%
t 1043
7.2%
y 1043
7.2%
o 971
6.7%
a 825
 
5.7%
n 681
 
4.7%
Other values (5) 2086
14.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 2115
14.6%
c 1652
11.4%
l 1405
9.7%
s 1362
9.4%
e 1261
8.7%
t 1043
7.2%
y 1043
7.2%
o 971
6.7%
a 825
 
5.7%
n 681
 
4.7%
Other values (5) 2086
14.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 2115
14.6%
c 1652
11.4%
l 1405
9.7%
s 1362
9.4%
e 1261
8.7%
t 1043
7.2%
y 1043
7.2%
o 971
6.7%
a 825
 
5.7%
n 681
 
4.7%
Other values (5) 2086
14.4%

price
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size63.0 KiB
medium
552 
low
295 
high
196 

Length

Max length6
Median length6
Mean length4.7756472
Min length3

Characters and Unicode

Total characters4981
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmedium
2nd rowlow
3rd rowmedium
4th rowmedium
5th rowlow

Common Values

ValueCountFrequency (%)
medium 552
52.9%
low 295
28.3%
high 196
 
18.8%

Length

2024-11-08T10:29:58.072499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T10:29:58.099266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
medium 552
52.9%
low 295
28.3%
high 196
 
18.8%

Most occurring characters

ValueCountFrequency (%)
m 1104
22.2%
i 748
15.0%
e 552
11.1%
d 552
11.1%
u 552
11.1%
h 392
 
7.9%
l 295
 
5.9%
o 295
 
5.9%
w 295
 
5.9%
g 196
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4981
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 1104
22.2%
i 748
15.0%
e 552
11.1%
d 552
11.1%
u 552
11.1%
h 392
 
7.9%
l 295
 
5.9%
o 295
 
5.9%
w 295
 
5.9%
g 196
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4981
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 1104
22.2%
i 748
15.0%
e 552
11.1%
d 552
11.1%
u 552
11.1%
h 392
 
7.9%
l 295
 
5.9%
o 295
 
5.9%
w 295
 
5.9%
g 196
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4981
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 1104
22.2%
i 748
15.0%
e 552
11.1%
d 552
11.1%
u 552
11.1%
h 392
 
7.9%
l 295
 
5.9%
o 295
 
5.9%
w 295
 
5.9%
g 196
 
3.9%

url
Categorical

High correlation  Imbalance 

Distinct11
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size61.6 KiB
?
881 
lacantinaslp.com
 
72
lunacafe.com.mx
 
34
reyecito.com
 
11
carlosandcharlies.com
 
10
Other values (6)
 
35

Length

Max length21
Median length1
Mean length3.3231064
Min length1

Characters and Unicode

Total characters3466
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row?
2nd row?
3rd row?
4th row?
5th row?

Common Values

ValueCountFrequency (%)
? 881
84.5%
lacantinaslp.com 72
 
6.9%
lunacafe.com.mx 34
 
3.3%
reyecito.com 11
 
1.1%
carlosandcharlies.com 10
 
1.0%
lasmananitas.com.mx 8
 
0.8%
sushi-itto.com.mx 8
 
0.8%
www.cenidet.edu.mx 6
 
0.6%
kikucuernavaca.com.mx 5
 
0.5%
no 4
 
0.4%

Length

2024-11-08T10:29:58.125667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
881
84.5%
lacantinaslp.com 72
 
6.9%
lunacafe.com.mx 34
 
3.3%
reyecito.com 11
 
1.1%
carlosandcharlies.com 10
 
1.0%
lasmananitas.com.mx 8
 
0.8%
sushi-itto.com.mx 8
 
0.8%
www.cenidet.edu.mx 6
 
0.6%
kikucuernavaca.com.mx 5
 
0.5%
no 4
 
0.4%

Most occurring characters

ValueCountFrequency (%)
? 881
25.4%
a 369
10.6%
c 309
 
8.9%
. 225
 
6.5%
n 223
 
6.4%
m 221
 
6.4%
l 210
 
6.1%
o 201
 
5.8%
i 128
 
3.7%
s 124
 
3.6%
Other values (14) 575
16.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3466
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
? 881
25.4%
a 369
10.6%
c 309
 
8.9%
. 225
 
6.5%
n 223
 
6.4%
m 221
 
6.4%
l 210
 
6.1%
o 201
 
5.8%
i 128
 
3.7%
s 124
 
3.6%
Other values (14) 575
16.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3466
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
? 881
25.4%
a 369
10.6%
c 309
 
8.9%
. 225
 
6.5%
n 223
 
6.4%
m 221
 
6.4%
l 210
 
6.1%
o 201
 
5.8%
i 128
 
3.7%
s 124
 
3.6%
Other values (14) 575
16.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3466
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
? 881
25.4%
a 369
10.6%
c 309
 
8.9%
. 225
 
6.5%
n 223
 
6.4%
m 221
 
6.4%
l 210
 
6.1%
o 201
 
5.8%
i 128
 
3.7%
s 124
 
3.6%
Other values (14) 575
16.6%

Rambience
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size66.1 KiB
familiar
967 
quiet
 
76

Length

Max length8
Median length8
Mean length7.7813998
Min length5

Characters and Unicode

Total characters8116
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfamiliar
2nd rowfamiliar
3rd rowfamiliar
4th rowfamiliar
5th rowfamiliar

Common Values

ValueCountFrequency (%)
familiar 967
92.7%
quiet 76
 
7.3%

Length

2024-11-08T10:29:58.153309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T10:29:58.179143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
familiar 967
92.7%
quiet 76
 
7.3%

Most occurring characters

ValueCountFrequency (%)
i 2010
24.8%
a 1934
23.8%
f 967
11.9%
m 967
11.9%
l 967
11.9%
r 967
11.9%
q 76
 
0.9%
u 76
 
0.9%
e 76
 
0.9%
t 76
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8116
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 2010
24.8%
a 1934
23.8%
f 967
11.9%
m 967
11.9%
l 967
11.9%
r 967
11.9%
q 76
 
0.9%
u 76
 
0.9%
e 76
 
0.9%
t 76
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8116
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 2010
24.8%
a 1934
23.8%
f 967
11.9%
m 967
11.9%
l 967
11.9%
r 967
11.9%
q 76
 
0.9%
u 76
 
0.9%
e 76
 
0.9%
t 76
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8116
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 2010
24.8%
a 1934
23.8%
f 967
11.9%
m 967
11.9%
l 967
11.9%
r 967
11.9%
q 76
 
0.9%
u 76
 
0.9%
e 76
 
0.9%
t 76
 
0.9%

franchise
Boolean

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
895 
True
148 
ValueCountFrequency (%)
False 895
85.8%
True 148
 
14.2%
2024-11-08T10:29:58.202213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

area
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size64.0 KiB
closed
918 
open
125 

Length

Max length6
Median length6
Mean length5.7603068
Min length4

Characters and Unicode

Total characters6008
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowclosed
2nd rowopen
3rd rowclosed
4th rowclosed
5th rowopen

Common Values

ValueCountFrequency (%)
closed 918
88.0%
open 125
 
12.0%

Length

2024-11-08T10:29:58.227076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T10:29:58.253536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
closed 918
88.0%
open 125
 
12.0%

Most occurring characters

ValueCountFrequency (%)
o 1043
17.4%
e 1043
17.4%
c 918
15.3%
l 918
15.3%
s 918
15.3%
d 918
15.3%
p 125
 
2.1%
n 125
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6008
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1043
17.4%
e 1043
17.4%
c 918
15.3%
l 918
15.3%
s 918
15.3%
d 918
15.3%
p 125
 
2.1%
n 125
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6008
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1043
17.4%
e 1043
17.4%
c 918
15.3%
l 918
15.3%
s 918
15.3%
d 918
15.3%
p 125
 
2.1%
n 125
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6008
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1043
17.4%
e 1043
17.4%
c 918
15.3%
l 918
15.3%
s 918
15.3%
d 918
15.3%
p 125
 
2.1%
n 125
 
2.1%

other_services
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size62.5 KiB
none
976 
Internet
 
36
variety
 
31

Length

Max length8
Median length4
Mean length4.2272291
Min length4

Characters and Unicode

Total characters4409
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownone
2nd rownone
3rd rownone
4th rowvariety
5th rownone

Common Values

ValueCountFrequency (%)
none 976
93.6%
Internet 36
 
3.5%
variety 31
 
3.0%

Length

2024-11-08T10:29:58.278385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T10:29:58.305527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
none 976
93.6%
internet 36
 
3.5%
variety 31
 
3.0%

Most occurring characters

ValueCountFrequency (%)
n 2024
45.9%
e 1079
24.5%
o 976
22.1%
t 103
 
2.3%
r 67
 
1.5%
I 36
 
0.8%
v 31
 
0.7%
a 31
 
0.7%
i 31
 
0.7%
y 31
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4409
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 2024
45.9%
e 1079
24.5%
o 976
22.1%
t 103
 
2.3%
r 67
 
1.5%
I 36
 
0.8%
v 31
 
0.7%
a 31
 
0.7%
i 31
 
0.7%
y 31
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4409
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 2024
45.9%
e 1079
24.5%
o 976
22.1%
t 103
 
2.3%
r 67
 
1.5%
I 36
 
0.8%
v 31
 
0.7%
a 31
 
0.7%
i 31
 
0.7%
y 31
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4409
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 2024
45.9%
e 1079
24.5%
o 976
22.1%
t 103
 
2.3%
r 67
 
1.5%
I 36
 
0.8%
v 31
 
0.7%
a 31
 
0.7%
i 31
 
0.7%
y 31
 
0.7%

parking_lot
Categorical

High correlation 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size62.6 KiB
none
480 
yes
327 
public
210 
valet parking
 
26

Length

Max length13
Median length6
Mean length4.3135187
Min length3

Characters and Unicode

Total characters4499
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpublic
2nd rownone
3rd rownone
4th rowyes
5th rownone

Common Values

ValueCountFrequency (%)
none 480
46.0%
yes 327
31.4%
public 210
20.1%
valet parking 26
 
2.5%

Length

2024-11-08T10:29:58.328312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T10:29:58.354912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
none 480
44.9%
yes 327
30.6%
public 210
19.6%
valet 26
 
2.4%
parking 26
 
2.4%

Most occurring characters

ValueCountFrequency (%)
n 986
21.9%
e 833
18.5%
o 480
10.7%
y 327
 
7.3%
s 327
 
7.3%
l 236
 
5.2%
i 236
 
5.2%
p 236
 
5.2%
b 210
 
4.7%
u 210
 
4.7%
Other values (8) 418
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4499
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 986
21.9%
e 833
18.5%
o 480
10.7%
y 327
 
7.3%
s 327
 
7.3%
l 236
 
5.2%
i 236
 
5.2%
p 236
 
5.2%
b 210
 
4.7%
u 210
 
4.7%
Other values (8) 418
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4499
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 986
21.9%
e 833
18.5%
o 480
10.7%
y 327
 
7.3%
s 327
 
7.3%
l 236
 
5.2%
i 236
 
5.2%
p 236
 
5.2%
b 210
 
4.7%
u 210
 
4.7%
Other values (8) 418
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4499
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 986
21.9%
e 833
18.5%
o 480
10.7%
y 327
 
7.3%
s 327
 
7.3%
l 236
 
5.2%
i 236
 
5.2%
p 236
 
5.2%
b 210
 
4.7%
u 210
 
4.7%
Other values (8) 418
9.3%

Interactions

2024-11-08T10:29:55.256036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-11-08T10:29:55.045608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-11-08T10:29:55.133301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-11-08T10:29:55.283874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-11-08T10:29:55.077474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-11-08T10:29:55.160630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-11-08T10:29:55.310305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-11-08T10:29:55.104306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-11-08T10:29:55.228172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2024-11-08T10:29:58.385562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
RambienceRcuisineaccessibilityalcoholareacitycountrydress_codefood_ratingfranchiselatitudelongitudeother_servicesparking_lotplaceIDpriceratingservice_ratingsmoking_areastateurlzip
Rambience1.0000.4380.1420.0410.1950.3080.1730.1190.0140.0520.0940.0890.5140.1800.0850.2250.0460.1010.0380.4180.2760.250
Rcuisine0.4381.0000.5800.6660.4900.4850.4860.4520.0750.5390.4940.4720.5530.5470.5860.4840.0000.0770.4650.4630.3600.421
accessibility0.1420.5801.0000.1040.3260.5960.2940.1680.0270.2220.3260.2680.2050.1470.5740.3850.0480.0230.3470.7920.2570.549
alcohol0.0410.6660.1041.0000.1240.2670.1900.1130.0520.2080.1550.1380.1490.2000.1990.3330.0000.0170.3560.2540.6140.538
area0.1950.4900.3260.1241.0000.6940.4190.2420.0000.0000.2360.2130.1180.2370.3700.4020.0000.0460.5490.6880.2040.558
city0.3080.4850.5960.2670.6941.0000.8510.4720.0570.4950.9050.9210.3760.2450.6820.4200.0840.0590.3800.8280.2380.510
country0.1730.4860.2940.1900.4190.8511.0000.2540.0000.2000.2400.3360.1930.1760.4270.3510.0000.0410.2020.8890.1340.616
dress_code0.1190.4520.1680.1130.2420.4720.2541.0000.0440.0440.1450.1140.0450.2380.3140.2110.0730.0870.2000.4580.5330.454
food_rating0.0140.0750.0270.0520.0000.0570.0000.0441.0000.0250.0560.0490.0510.0000.0210.0410.5900.5550.0000.0490.0460.121
franchise0.0520.5390.2220.2080.0000.4950.2000.0440.0251.0000.3620.3220.1240.2190.2610.1460.0290.0000.3730.4900.2050.641
latitude0.0940.4940.3260.1550.2360.9050.2400.1450.0560.3621.0000.0300.1180.072-0.1030.2140.0930.0670.2910.9120.4320.505
longitude0.0890.4720.2680.1380.2130.9210.3360.1140.0490.3220.0301.0000.1570.106-0.2860.2850.0240.0000.4110.9330.4120.555
other_services0.5140.5530.2050.1490.1180.3760.1930.0450.0510.1240.1180.1571.0000.2050.0820.0960.0320.0530.1510.2930.2430.115
parking_lot0.1800.5470.1470.2000.2370.2450.1760.2380.0000.2190.0720.1060.2051.0000.2030.2760.0000.0510.3790.2890.2670.497
placeID0.0850.5860.5740.1990.3700.6820.4270.3140.0210.261-0.103-0.2860.0820.2031.0000.3890.0520.0240.3500.8470.2400.590
price0.2250.4840.3850.3330.4020.4200.3510.2110.0410.1460.2140.2850.0960.2760.3891.0000.0950.1210.2510.4670.4790.621
rating0.0460.0000.0480.0000.0000.0840.0000.0730.5900.0290.0930.0240.0320.0000.0520.0951.0000.5820.0570.0690.0460.114
service_rating0.1010.0770.0230.0170.0460.0590.0410.0870.5550.0000.0670.0000.0530.0510.0240.1210.5821.0000.0240.0660.0640.108
smoking_area0.0380.4650.3470.3560.5490.3800.2020.2000.0000.3730.2910.4110.1510.3790.3500.2510.0570.0241.0000.4190.2490.442
state0.4180.4630.7920.2540.6880.8280.8890.4580.0490.4900.9120.9330.2930.2890.8470.4670.0690.0660.4191.0000.2290.530
url0.2760.3600.2570.6140.2040.2380.1340.5330.0460.2050.4320.4120.2430.2670.2400.4790.0460.0640.2490.2291.0000.649
zip0.2500.4210.5490.5380.5580.5100.6160.4540.1210.6410.5050.5550.1150.4970.5900.6210.1140.1080.4420.5300.6491.000

Missing values

2024-11-08T10:29:55.365979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-08T10:29:55.444931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

userIDplaceIDratingfood_ratingservice_ratingRcuisinelatitudelongitudethe_geom_meternameaddresscitystatecountryfaxzipalcoholsmoking_areadress_codeaccessibilitypriceurlRambiencefranchiseareaother_servicesparking_lot
0U1077135085222Fast_Food22.150802-100.9826800101000020957F00009F823DA6094858C18A2D4D37F9A44B41Tortas Locas HipocampoVenustiano Carranza 719 CentroSan Luis PotosiSLPMexico?78000No_Alcohol_Servednot permittedinformalno_accessibilitymedium?familiarfclosednonepublic
1U1077132825222Mexican22.147392-100.9830920101000020957F00001AD016568C4858C1243261274BA54B41puesto de tacosesquina santos degollado y leon guzmans.l.p.s.l.p.mexico?78280No_Alcohol_Servednoneinformalcompletelylow?familiarfopennonenone
2U1077135060122Seafood22.156883-100.9784850101000020957F00004C95C918394758C17A5C44896AA34B41Restaurante Marisco SamIgnacio Allende 785 CentroSan Luis PotosiSLPMexico?78310No_Alcohol_Servednoneinformalno_accessibilitymedium?familiarfclosednonenone
3U1068135104112Mexican23.752982-99.1684340101000020957F00007CDF5EAFC58157C1645743B23E4F4941vips??????Full_Barnot permittedinformalcompletelymedium?familiartclosedvarietyyes
4U1068132740000Mexican23.752197-99.1666320101000020957F000027A30471EE8157C1AC17D61EC84E4941Carreton de Flautas y Migadas?Ciudad VictoriaTamaulipasMexico??No_Alcohol_Servedpermittedinformalcompletelylow?familiarfopennonenone
5U1068132663111Mexican23.752511-99.1669540101000020957F0000FDF8D26EE08157C1FEDB6A1FDB4E4941tacos abi?victoriatamaulipasmexico??No_Alcohol_Servednoneinformalcompletelylow?familiarfclosednonenone
6U1068132732000Mexican23.754357-99.1712880101000020957F00008A20E615808157C16272FECBF84F4941Taqueria EL amigoCalle Mezquite Fracc FramboyanesCd VictoriaTamaulipasMexico?87018No_Alcohol_Servednonecasualcompletelylow?familiarfopennonenone
7U1068132630111Mexican23.752931-99.1644730101000020957F000047206572DE8157C1D3315028254E4941palomo tecblvrd emilio portes gilvictoriatamaulipas???No_Alcohol_Servednoneinformalcompletelylow?familiarfclosednonenone
8U1067132584222Mexican23.752365-99.1652880101000020957F000048C65BA7EF8157C177F2344D664E4941Gorditas Dona Tota??????No_Alcohol_Servednot permittedinformalcompletelymedium?familiartclosednoneyes
9U1067132733111Pizzeria23.752707-99.1625650101000020957F0000F2F4110CF28157C1DCFD7C1BA04D4941Little Cesarz?Ciudad VictoriaTamaulipasMexico??No_Alcohol_Servednot permittedinformalcompletelymedium?familiartclosednoneyes
userIDplaceIDratingfood_ratingservice_ratingRcuisinelatitudelongitudethe_geom_meternameaddresscitystatecountryfaxzipalcoholsmoking_areadress_codeaccessibilitypriceurlRambiencefranchiseareaother_servicesparking_lot
1033U1130132706121Mexican23.729216-99.1323570101000020957F00000F14BF6B2C8657C1963CCB8E5C464941Gorditas Dona TotaZaragoza entre Francisco Zarco y Lopez VelardeCd. VictoriaTamaulipasMexico??No_Alcohol_Servednot permittedinformalno_accessibilitymedium?familiartclosednonepublic
1034U1043132608111Mexican23.758805-99.1651300101000020957F0000A15C8F5FF78057C1C93CEA9E0A4E4941Hamburguesas La pericacd. miervictoriaTamaulipasMexico??No_Alcohol_Servedpermittedinformalcompletelylow?quiettopennonepublic
1035U1043132609121Fast_Food23.760268-99.1658650101000020957F0000A478418BBA8057C133851EB22C4E4941Pollo_Frito_Buenos_AirestampicovictoriaTamaulipasMexico??No_Alcohol_Servednot permittedinformalcompletelylow?quiettclosednoneyes
1036U1011132717221Fast_Food23.731860-99.1504360101000020957F0000B21C3F6C5E8557C188ACCF0A444B4941tortas hawai??????No_Alcohol_Servednot permittedinformalpartiallymedium?familiarfclosednonepublic
1037U1043132613111Mexican23.752903-99.1650760101000020957F00008EBA2D06DC8157C194E03B7B504E4941carnitas_matalic. Emilio portes gilvictoriaTamaulipasMexico??No_Alcohol_Servedpermittedinformalcompletelymedium?familiartclosednoneyes
1038U1043132732111Mexican23.754357-99.1712880101000020957F00008A20E615808157C16272FECBF84F4941Taqueria EL amigoCalle Mezquite Fracc FramboyanesCd VictoriaTamaulipasMexico?87018No_Alcohol_Servednonecasualcompletelylow?familiarfopennonenone
1039U1043132630111Mexican23.752931-99.1644730101000020957F000047206572DE8157C1D3315028254E4941palomo tecblvrd emilio portes gilvictoriatamaulipas???No_Alcohol_Servednoneinformalcompletelylow?familiarfclosednonenone
1040U1011132715110Mexican23.732423-99.1586600101000020957F00004609B96F198557C11973490A874D4941tacos de la estacion??????No_Alcohol_Servednoneinformalno_accessibilitylow?quietfopennonenone
1041U1068132733110Pizzeria23.752707-99.1625650101000020957F0000F2F4110CF28157C1DCFD7C1BA04D4941Little Cesarz?Ciudad VictoriaTamaulipasMexico??No_Alcohol_Servednot permittedinformalcompletelymedium?familiartclosednoneyes
1042U1068132594111Mexican23.752168-99.1657090101000020957F00003AE4C6DBF48157C1CE38ECC1864E4941tacos de barbacoa enfrente del Tec??????No_Alcohol_Servednot permittedinformalcompletelylow?familiarfopennonepublic